Design documents and code for the pandas 2.0 effort. This repository contains a non-destructive fork of upstream pandas
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Latest commit cf1bc81 Dec 14, 2016 @wesm wesm [pandas 2.0] Use C++ exceptions instead of Status values for error re…

This also includes some modest code reorg. Since this has no
functional changes I'm going to merge and proceed with work in a new

Author: Wes McKinney <>

Closes #64 from wesm/cpp-exceptions and squashes the following commits:

4898e1f [Wes McKinney] Other fixes. use NumPy 1.7 API consistently
5ecd466 [Wes McKinney] Fix up Cython code also
bf08331 [Wes McKinney] Use exceptions instead of Status. Consolidate array types into array.h
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.github Update Github issue template (#14268) Sep 23, 2016
LICENSES [pandas 2.0] development scaffolding Dec 12, 2016
asv_bench PERF: faster grouping Sep 27, 2016
bench CLN: Removed SparsePanel Jul 26, 2016
build-support [pandas 2.0] Add "make format" target using clang-format Dec 12, 2016
ci [pandas 2.0] development scaffolding Dec 12, 2016
cmake_modules [pandas 2.0] Share low level C++ utility code (memory allocation, err… Dec 12, 2016
codegen [pandas 2.0] Miscellaneous prototyping on scalar/array/expression obj… Dec 12, 2016
conda.recipe CI: remove leading v from built versions Mar 10, 2016
doc BUG: set_levels set illegal levels. (#14236) Oct 10, 2016
pandas [pandas 2.0] Use C++ exceptions instead of Status values for error re… Dec 14, 2016
scripts [pandas 2.0] Add basic Circle CI setup Dec 12, 2016
src [pandas 2.0] Use C++ exceptions instead of Status values for error re… Dec 14, 2016
vb_suite CLN: Removed pandas.util.testing.choice Mar 2, 2016
.binstar.yml update conda recipe to make import only tests Sep 6, 2015
.coveragerc TST: Omit tests folders from coverage Mar 31, 2016
.gitattributes CI: use versioneer, for PEP440 version strings #9518 Jul 6, 2015
.gitignore BUG: agg() function on groupby dataframe changes dtype of datetime64[… Aug 6, 2016
.travis.yml [pandas 2.0] development scaffolding Dec 12, 2016
CMakeLists.txt [pandas 2.0] Use C++ exceptions instead of Status values for error re… Dec 14, 2016
LICENSE RLS: Version 0.10.0 final Dec 17, 2012 CI: use versioneer, for PEP440 version strings #9518 Jul 6, 2015
Makefile BLD: spring cleaning on Makefile Apr 6, 2014 [pandas 2.0] Update (#59) Dec 12, 2016 DOC: update to point to stable whatsnew Jul 14, 2014
appveyor.yml BLD: add in build conflict resolution to appeveyor.yml Sep 5, 2016 BLD: edit release script Mar 11, 2016
circle.yml [pandas 2.0] Add basic Circle CI setup Dec 12, 2016
codecov.yml BUG: Correct KeyError from matplotlib when processing Series yerr May 13, 2016 add args to Nov 20, 2015
setup.cfg PEP: pandas/core round 2 with yapf and add to setup.cfg Jan 16, 2016 [pandas 2.0] development scaffolding Dec 12, 2016
test.bat TST: add windows test.bat Sep 3, 2015 micro + nanosecond time support Sep 30, 2013 TST: and should skip network tests Oct 19, 2013 TST: and should skip network tests Oct 19, 2013 BLD: make work on OSX too Sep 9, 2013 TST: pass cmd line args to test scripts so can append -v etc May 7, 2012
tox.ini COMPAT: drop suppport for python 2.6, #7718 Jan 7, 2016 CI: use versioneer, for PEP440 version strings #9518 Jul 6, 2015

pandas: powerful Python data analysis toolkit

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What is it

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.

Main Features

Here are just a few of the things that pandas does well:

  • Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
  • Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
  • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
  • Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
  • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
  • Intuitive merging and joining data sets
  • Flexible reshaping and pivoting of data sets
  • Hierarchical labeling of axes (possible to have multiple labels per tick)
  • Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
  • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.

Where to get it

The source code is currently hosted on GitHub at:

Binary installers for the latest released version are available at the Python package index and on conda.

# conda
conda install pandas
# or PyPI
pip install pandas


See the full installation instructions for recommended and optional dependencies.

Installation from sources

To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from pypi:

pip install cython

In the pandas directory (same one where you found this file after cloning the git repo), execute:

python install

or for installing in development mode:

python develop

Alternatively, you can use pip if you want all the dependencies pulled in automatically (the -e option is for installing it in development mode):

pip install -e .

On Windows, you will need to install MinGW and execute:

python build --compiler=mingw32
python install

See for more information.




The official documentation is hosted on

The Sphinx documentation should provide a good starting point for learning how to use the library. Expect the docs to continue to expand as time goes on.


Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then.

Discussion and Development

Since pandas development is related to a number of other scientific Python projects, questions are welcome on the scipy-user mailing list. Specialized discussions or design issues should take place on the PyData mailing list / Google group:!forum/pydata